In this work, we present
a novel online predictor for protein acetylation sites prediction
of PAIL, Prediction of Acetylation on Internal Lysines. We have
manually mined scientific literature to collect 249
experimentally verified acetylation sites of 92
distinct proteins. Then the BDM (Bayesian Discriminant Method)
algorithm has been employed. The window length of a potential
acetylated peptide has been optimized as 13. The accuracy
of PAIL is highly encouraging with 85.13%,
87.97% and 89.21% at low, medium and high thresholds,
respectively. Both Jack-knife validation and n-fold
(6-, 8-, and 10-fold) cross-validation have been performed to
show that the PAIL is accurate and robust. In this regard, we
propose that PAIL could be a useful tool for experimentalists.
And the prediction results of PAIL might also be insightful
for further experimental design. For convenience, we have implemented
the prediction system in a web server, which is available at:
http://bdmpail.biocuckoo.org/.

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